| 基于GA-WNN的极化SAR海洋溢油检测方法研究 |
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| 引用本文： | 陈伟民,丁亚雄,宋冬梅,王 斌,刘善伟,甄宗晋,张 婷,杨 敏.基于GA-WNN的极化SAR海洋溢油检测方法研究[J].海洋科学,2018,42(1):70-81. |
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| 作者姓名： | 陈伟民 丁亚雄 宋冬梅 王 斌 刘善伟 甄宗晋 张 婷 杨 敏 |
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| 作者单位： | 中国石油大学(华东)地球科学与技术学院;中国石油大学(华东)研究生院;海洋矿物资源实验室青岛海洋科学技术国家实验室;国家海洋局第一海洋研究所;国家海洋局北海分局 |
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| 基金项目： | 国家重点研发计划(2017YFC1405600); 国家自然科学基金项目(41772350, 61371189, 41706208, 41701513) |
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| 摘 要： | 海洋溢油对海洋生态和人类生活带来严重的影响。由于合成孔径雷达(Synthetic Aperture Radar,SAR)具有全天时全天候的工作能力,在海洋溢油检测中发挥重要作用。目前,极化SAR是SAR探测技术的先进手段。本文利用6个极化特征进行溢油检测,通过对比分析这些特征对不同溢油的检测能力,得出单一极化特征在溢油检测中存在不足。通过J-M特征优选方法,提取出溢油检测识别度较高的特征影像,并利用遗传算法优化的小波神经网络(Genetic Algorithm-Wavelet Neural Network,GA-WNN)进行溢油检测。利用2套Radarsat-2全极化数据进行了方法验证,结果表明,该方法优于其他检测方法,溢油检测精度分别达到90.31%和95.42%。
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| 关 键 词： | Radarsat-2 SAR 极化特征 遗传算法 小波神经网络 海洋溢油 |
| 收稿时间： | 2017/10/11 0:00:00 |
| 修稿时间： | 2017/12/10 0:00:00 |
Ocean oil-spill detection using Pol-SAR data based on GAWNN |
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| CHEN Wei-min,DING Ya-xiong,SONG Dong-mei,WANG Bin,LIU Shan-wei,ZHEN Zong-jin,ZHANG Ting and YANG Min.Ocean oil-spill detection using Pol-SAR data based on GAWNN[J].Marine Sciences,2018,42(1):70-81. |
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| Authors: | CHEN Wei-min DING Ya-xiong SONG Dong-mei WANG Bin LIU Shan-wei ZHEN Zong-jin ZHANG Ting and YANG Min |
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| Abstract: | Ocean oil spills seriously threaten both the marine environment and human activity. Synthetic aperture radar (SAR) plays an important role in ocean oil-spill detection due to its all-weather and day-and-night capabilities. Polarimetric SAR (Pol-SAR) is an advanced SAR detection technology that makes full use of the backscattering characteristics between SAR channels and has demonstrated obvious advantages in ocean oil-spill detection. We conducted experiments to investigate six polarimetric characteristics, based on the fact that a single characteristic can be inadequate in oil-spill detection with respect to the analysis of different features. Using the J-M distance index method to perform feature selection, we then used the genetic-algorithm-optimized wavelet neural network (GA-WNN) to detect oil spills. The experimental results from two sets of Radarsat-2 data confirm the superior accuracy of the proposed method with regard to oil-spill detection, i.e., 90.31% and 95.42%, espectively. |
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| Keywords: | Radarsat-2 SAR Polarimetric SAR Characteristic Genetic Algorithm Wavelet Neural Network Ocean oil spill |
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